{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yk6vVmtuVJjl"
      },
      "source": [
        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
        "<div class=\"align-center\">\n",
        "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
        "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
        "</div>\n",
        "\n",
        "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
        "\n",
        "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LChpNPYlVJjn"
      },
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRcxnFhxVJjn"
      },
      "source": [
        "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n",
        "\n",
        "Read our **[Qwen3 Guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n",
        "\n",
        "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env HUGGINGFACE_TOKEN=替换为自己的HuggingFace Key"
      ],
      "metadata": {
        "id": "f9A3ehrsyFIP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLp9eB6MVJjn"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YOZzBSprVJjp"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "import os\n",
        "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
        "    !pip install unsloth\n",
        "else:\n",
        "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
        "    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo\n",
        "    !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
        "    !pip install transformers==4.51.3\n",
        "    !pip install --no-deps unsloth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iajq1W8ipjyK"
      },
      "source": [
        "### Unsloth"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "QmUBVEnvCDJv",
        "outputId": "69f57d67-747e-4081-8408-fb614a97d87f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2025.6.2: Fast Qwen3 patching. Transformers: 4.51.3.\n",
            "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
            " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.41G [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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        },
        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
            ],
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          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "674aa6cfdf554d0b82237ede970c661e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "chat_template.jinja:   0%|          | 0.00/4.67k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bc16a41cb244ead8b72ed48cc881061"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "\n",
        "fourbit_models = [\n",
        "    \"unsloth/Qwen3-1.7B-unsloth-bnb-4bit\", # Qwen 14B 2x faster\n",
        "    \"unsloth/Qwen3-4B-unsloth-bnb-4bit\",\n",
        "    \"unsloth/Qwen3-8B-unsloth-bnb-4bit\",\n",
        "    \"unsloth/Qwen3-14B-unsloth-bnb-4bit\",\n",
        "    \"unsloth/Qwen3-32B-unsloth-bnb-4bit\",\n",
        "\n",
        "    # 4bit dynamic quants for superior accuracy and low memory use\n",
        "    \"unsloth/gemma-3-12b-it-unsloth-bnb-4bit\",\n",
        "    \"unsloth/Phi-4\",\n",
        "    \"unsloth/Llama-3.1-8B\",\n",
        "    \"unsloth/Llama-3.2-3B\",\n",
        "    \"unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit\" # [NEW] We support TTS models!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    # model_name = \"unsloth/Qwen3-14B\",\n",
        "    # model_name = \"Qwen/Qwen3-1.7B-Base\",\n",
        "    model_name = \"Qwen/Qwen3-1.7B\",\n",
        "    max_seq_length = 4096,   # Context length - can be longer, but uses more memory\n",
        "    load_in_4bit = True,     # 4bit uses much less memory\n",
        "    load_in_8bit = False,    # A bit more accurate, uses 2x memory\n",
        "    full_finetuning = False, # We have full finetuning now!\n",
        "    # token = \"hf_...\",      # use one if using gated models\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bZsfBuZDeCL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec1e1ea6-7819-4686-897a-454fb9b6591b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2025.6.2 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 32,           # Choose any number > 0! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 32,  # Best to choose alpha = rank or rank*2\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,   # We support rank stabilized LoRA\n",
        "    loftq_config = None,  # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "Qwen3 has both reasoning and a non reasoning mode. So, we should use 2 datasets:\n",
        "\n",
        "1. We use the [Open Math Reasoning]() dataset which was used to win the [AIMO](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/leaderboard) (AI Mathematical Olympiad - Progress Prize 2) challenge! We sample 10% of verifiable reasoning traces that used DeepSeek R1, and whicht got > 95% accuracy.\n",
        "\n",
        "2. We also leverage [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we need to convert it to HuggingFace's normal multiturn format as well."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "df = pd.read_excel('https://raw.githubusercontent.com/Steven-Luo/MasteringRAG/main/outputs/v1_1_20240811/question_answer.xlsx')\n",
        "\n",
        "# 仅使用训练集进行训练\n",
        "df = df[df['context'].notnull() & (df['dataset'] == 'train')]"
      ],
      "metadata": {
        "id": "cKAV9DcMIrVY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['context'].notnull() & (df['dataset'] == 'train')]"
      ],
      "metadata": {
        "id": "NnGOvh2uIuz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_sample(row):\n",
        "    prompt = \"\"\"\n",
        "你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\n",
        "你的任务是根据所获取的信息片段（<context></context>之间的内容）回答问题。\n",
        "回答保持简洁，不必重复问题，不要添加描述性解释和与答案无关的任何内容。\n",
        "已知信息：\n",
        "<context>\n",
        "{{knowledge}}\n",
        "</context>\n",
        "\n",
        "问题：{{query}}\n",
        "请回答：\n",
        "\"\"\".replace('{{knowledge}}', row['context']).replace('{{query}}', row['question']).strip() + '/no_think'\n",
        "    return prompt\n",
        "\n",
        "df['instruction'] = df.apply(lambda row: build_sample(row), axis=1)\n",
        "# instruction中设置了/no_think，返回结果与instruction对应\n",
        "df['output'] = df['answer'].apply(lambda x: '<think>\\n</think>' + x)"
      ],
      "metadata": {
        "id": "UE_nrpDlIQAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OwobpO1RQKO6",
        "outputId": "1b9dc73d-fb80-40fb-ea89-397c03c5f568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   uuid            question  \\\n",
              "0  11cc0dac-bbd0-4f65-b35e-0e5da0c89b36  2023年全球经济增长的特点是什么?   \n",
              "1  11cc0dac-bbd0-4f65-b35e-0e5da0c89b36    全球贸易在2023年的表现如何?   \n",
              "3  11cc0dac-bbd0-4f65-b35e-0e5da0c89b36     2023年全球股市的表现如何?   \n",
              "\n",
              "                                              answer  \\\n",
              "0  2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，而新兴经济体整体表...   \n",
              "1            2023年全球贸易增长乏力，各国生产景气度逐渐回落，内需对经济的拉动作用减弱。   \n",
              "3                         2023年全球股市表现总体好于预期，但区域分化明显。   \n",
              "\n",
              "                                             context  \\\n",
              "0  2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，新兴经济体整体表现稳定。   \n",
              "1                 全球贸易增长乏力，各国生产景气度逐渐回落，内需对经济的拉动作用减弱。   \n",
              "3   欧美央行货币政策紧缩态势放缓，美元指数高位震荡后走弱，全球股市表现总体好于预期，但区域分化明显。   \n",
              "\n",
              "                                                 doc   qa_type  score  \\\n",
              "0  研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...  detailed      5   \n",
              "1  研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...  detailed      5   \n",
              "3  研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...  detailed      5   \n",
              "\n",
              "                                        score_reason dataset  \\\n",
              "0  问题针对2023年的全球经济特点提出了明确的询问，回答也直接针对问题给出了具体的信息反馈，没...   train   \n",
              "1  问题询问了具体的年度全球经济状况，回答也直接针对该问题提供了明确的信息，没有提及无关内容或章...   train   \n",
              "3  问题询问了具体的年度股市表现，而回答也直接针对问题给出了概括性的描述，没有提及无关内容或章节引用。   train   \n",
              "\n",
              "                                         instruction  \\\n",
              "0  你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...   \n",
              "1  你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...   \n",
              "3  你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...   \n",
              "\n",
              "                                              output  \n",
              "0  <think>\\n</think>2023年全球经济增长动力持续回落，各国复苏分化，发达经济...  \n",
              "1  <think>\\n</think>2023年全球贸易增长乏力，各国生产景气度逐渐回落，内需对...  \n",
              "3        <think>\\n</think>2023年全球股市表现总体好于预期，但区域分化明显。  "
            ],
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              "      <th>0</th>\n",
              "      <td>11cc0dac-bbd0-4f65-b35e-0e5da0c89b36</td>\n",
              "      <td>2023年全球经济增长的特点是什么?</td>\n",
              "      <td>2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，而新兴经济体整体表...</td>\n",
              "      <td>2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，新兴经济体整体表现稳定。</td>\n",
              "      <td>研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...</td>\n",
              "      <td>detailed</td>\n",
              "      <td>5</td>\n",
              "      <td>问题针对2023年的全球经济特点提出了明确的询问，回答也直接针对问题给出了具体的信息反馈，没...</td>\n",
              "      <td>train</td>\n",
              "      <td>你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...</td>\n",
              "      <td>&lt;think&gt;\\n&lt;/think&gt;2023年全球经济增长动力持续回落，各国复苏分化，发达经济...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11cc0dac-bbd0-4f65-b35e-0e5da0c89b36</td>\n",
              "      <td>全球贸易在2023年的表现如何?</td>\n",
              "      <td>2023年全球贸易增长乏力，各国生产景气度逐渐回落，内需对经济的拉动作用减弱。</td>\n",
              "      <td>全球贸易增长乏力，各国生产景气度逐渐回落，内需对经济的拉动作用减弱。</td>\n",
              "      <td>研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...</td>\n",
              "      <td>detailed</td>\n",
              "      <td>5</td>\n",
              "      <td>问题询问了具体的年度全球经济状况，回答也直接针对该问题提供了明确的信息，没有提及无关内容或章...</td>\n",
              "      <td>train</td>\n",
              "      <td>你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...</td>\n",
              "      <td>&lt;think&gt;\\n&lt;/think&gt;2023年全球贸易增长乏力，各国生产景气度逐渐回落，内需对...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11cc0dac-bbd0-4f65-b35e-0e5da0c89b36</td>\n",
              "      <td>2023年全球股市的表现如何?</td>\n",
              "      <td>2023年全球股市表现总体好于预期，但区域分化明显。</td>\n",
              "      <td>欧美央行货币政策紧缩态势放缓，美元指数高位震荡后走弱，全球股市表现总体好于预期，但区域分化明显。</td>\n",
              "      <td>研究院\\n全球经济金融展望报告\\n要点2024年年报（总第57期） 报告日期：2023年12...</td>\n",
              "      <td>detailed</td>\n",
              "      <td>5</td>\n",
              "      <td>问题询问了具体的年度股市表现，而回答也直接针对问题给出了概括性的描述，没有提及无关内容或章节引用。</td>\n",
              "      <td>train</td>\n",
              "      <td>你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取...</td>\n",
              "      <td>&lt;think&gt;\\n&lt;/think&gt;2023年全球股市表现总体好于预期，但区域分化明显。</td>\n",
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       ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 277,\n        \"samples\": [\n          \"<think>\\n</think>\\u5168\\u7403\\u670d\\u52a1\\u4e1aPMI\\u6307\\u6570\\u4ece\\u4e8c\\u5b63\\u5ea6\\u5f00\\u59cb\\u660e\\u663e\\u56de\\u843d\\uff0c\\u4ece5\\u6708\\u768455.5%\\u964d\\u81f310\\u6708\\u768450.4%\\u3002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from torch.utils.data import Dataset\n",
        "\n",
        "# class RAGDataset(Dataset):\n",
        "#     def __init__(self, df):\n",
        "#         self.df = df\n",
        "\n",
        "#     def __len__(self):\n",
        "#         return len(self.df)\n",
        "\n",
        "#     def __getitem__(self, idx):\n",
        "#         row = self.df.iloc[idx]\n",
        "#         return {\n",
        "#             'instruction': row['instruction'],\n",
        "#             'output': row['answer']\n",
        "#         }"
      ],
      "metadata": {
        "id": "vSNYszZIJloe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5kyTw2n1edte"
      },
      "outputs": [],
      "source": [
        "# from datasets import load_dataset\n",
        "# reasoning_dataset = load_dataset(\"unsloth/OpenMathReasoning-mini\", split = \"cot\")\n",
        "# non_reasoning_dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "rag_dataset = Dataset.from_pandas(df[['instruction', 'output']])"
      ],
      "metadata": {
        "id": "U2AqWP61J5VH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTZICZtie3lQ"
      },
      "source": [
        "Let's see the structure of both datasets:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rag_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ps06aTVaKf3_",
        "outputId": "58d1ffc6-bc19-497e-e2fa-bbf5a4e51572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['instruction', 'output', '__index_level_0__'],\n",
              "    num_rows: 278\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjgH3lt0e2Sz",
        "outputId": "28996bfd-e1ba-46cf-c4f7-0023d3c67150"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'instruction': '你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取的信息片段（<context></context>之间的内容）回答问题。\\n回答保持简洁，不必重复问题，不要添加描述性解释和与答案无关的任何内容。\\n已知信息：\\n<context>\\n2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，新兴经济体整体表现稳定。\\n</context>\\n\\n问题：2023年全球经济增长的特点是什么?\\n请回答：/no_think',\n",
              " 'output': '<think>\\n</think>2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，而新兴经济体整体表现稳定。',\n",
              " '__index_level_0__': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "rag_dataset[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX8H3urDe00l"
      },
      "source": [
        "We now convert the reasoning dataset into conversational format:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LjY75GoYUCB8"
      },
      "outputs": [],
      "source": [
        "def generate_conversation(examples):\n",
        "    problems  = examples[\"instruction\"]\n",
        "    solutions = examples[\"output\"]\n",
        "    conversations = []\n",
        "    for problem, solution in zip(problems, solutions):\n",
        "        conversations.append([\n",
        "            {\"role\" : \"user\",      \"content\" : problem},\n",
        "            {\"role\" : \"assistant\", \"content\" : solution},\n",
        "        ])\n",
        "    return { \"conversations\": conversations, }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gbh19fTOfHDB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "52ef853d927148bbb8da8d195951635b",
            "0dd55000b1da45d2b6ee3c60e633b26c",
            "a0151daf89ed4a09908e0f958bef34d1",
            "0bbee5efda76479b8d03ef0842ab4c5c",
            "77848850862849d4b3cb35cdc6bbaa40",
            "66c2c8337227400ab22532dfd21df19b",
            "ae6127f1347e4348916aeb2b7e5e0f82",
            "bb2d86331ab54516a8f210b60bf1c0e7",
            "27695d34019d4d75b65520851b9739ef",
            "fc889975e9e641f982ace72e78512b2f",
            "33a076805387436ca01b9156337232be"
          ]
        },
        "outputId": "65022c22-858d-410a-8aba-02db363ea5fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/278 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52ef853d927148bbb8da8d195951635b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "rag_dataset_conversation = tokenizer.apply_chat_template(\n",
        "    rag_dataset.map(generate_conversation, batched = True)[\"conversations\"],\n",
        "    tokenize = False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rag_dataset_conversation[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "bEB4ftQOL807",
        "outputId": "c2a04bd0-fee8-41a9-dac4-90d662b33bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|im_start|>user\\n你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取的信息片段（<context></context>之间的内容）回答问题。\\n回答保持简洁，不必重复问题，不要添加描述性解释和与答案无关的任何内容。\\n已知信息：\\n<context>\\n2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，新兴经济体整体表现稳定。\\n</context>\\n\\n问题：2023年全球经济增长的特点是什么?\\n请回答：/no_think<|im_end|>\\n<|im_start|>assistant\\n<think>\\n\\n</think>\\n\\n2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，而新兴经济体整体表现稳定。<|im_end|>\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset.from_pandas(pd.DataFrame({'text': rag_dataset_conversation}))\n",
        "train_dataset.name = 'text'"
      ],
      "metadata": {
        "id": "aj9V48kBL4BA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "19b37d7776b1433b8377e2db6d70c388",
            "e974d46ea8c645078b5b8008944218f9",
            "433f4d42431e4a45a9272a2dd619bc18",
            "65505ead1c71454fb17c4e328e4eceaf",
            "0690b5457017455bbb27086fdc79c703",
            "0ecb51e3d9044a788c3bb383e4559c48",
            "7dbdee24458d489d98c8b0fff3159dda",
            "6e8e804925ed4430a4ee725e694672f2",
            "aa4a681928ad4cb2899ce9e09f278f97",
            "a58a15e8054a402192894f968f6cb6ba",
            "0657c5b87fce40e69448112d73d774d1"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "fc5679f2-73d7-4548-86b7-725147f7454c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/278 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19b37d7776b1433b8377e2db6d70c388"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from trl import SFTTrainer, SFTConfig\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = train_dataset,\n",
        "    eval_dataset = None, # Can set up evaluation!\n",
        "    args = SFTConfig(\n",
        "        dataset_text_field = \"text\",\n",
        "        per_device_train_batch_size = 32,\n",
        "        gradient_accumulation_steps = 1, # Use GA to mimic batch size!\n",
        "        warmup_steps = 5,\n",
        "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
        "        max_steps = 200,\n",
        "        learning_rate = 2e-4, # Reduce to 2e-5 for long training runs\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"cosine\",\n",
        "        seed = 3407,\n",
        "        report_to = \"none\", # Use this for WandB etc\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "523051f4-3f9a-44d8-f141-cce20b7d7e9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.741 GB.\n",
            "2.01 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "# @title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9fa371ShyhB"
      },
      "source": [
        "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "19fceb60-6852-41c4-9cc1-8c766731fff2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
            "   \\\\   /|    Num examples = 278 | Num Epochs = 23 | Total steps = 200\n",
            "O^O/ \\_/ \\    Batch size per device = 32 | Gradient accumulation steps = 1\n",
            "\\        /    Data Parallel GPUs = 1 | Total batch size (32 x 1 x 1) = 32\n",
            " \"-____-\"     Trainable parameters = 34,865,152/1,700,000,000 (2.05% trained)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 16:25, Epoch 22/23]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.757400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.666900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.687500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.414300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.933200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.871200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.451300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.148500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.980700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.729400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.521500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.358000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.335000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.122000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.978600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.080500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.890300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.887300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.847500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.804800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.931700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.937000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.719400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.769700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.834600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.919200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.764700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.827600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.673000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.715400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.877700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.780600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.681400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.735300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.799300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.707700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.733300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.811800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.729500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.628100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.678300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.583500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.618700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.759700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.652700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.637300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.616900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.657800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.647300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.548800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.636800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.582300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.605600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.541500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.517900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.521000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.478400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.505700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.599600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.481300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.600700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.489400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.501400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.417000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.446500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.415300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.473300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.431500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.438300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.373100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.416700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.356200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.384000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.358200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.317000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.376900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.321900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.375200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.289900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.306700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.303300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.294200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.202900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.257700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.292700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.286200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.304300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.211100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.247800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.211900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.209900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.203500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.221300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.209000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.181400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.189700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.171400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.152100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.174100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.164000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.131100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.168500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>0.139200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.160500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.168300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.143300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.126100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>0.112100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>0.126600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>0.111200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>0.105400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>0.113900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>0.115300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>0.128600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>0.095000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.106600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.089700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>0.107500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>0.081300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>0.095400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>0.098200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.083900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>0.112100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>0.068900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>0.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>0.079200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.087500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>0.072900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>0.085800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>0.076600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>0.079100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>0.077300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>0.082100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>0.063900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>0.058700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>0.068500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.065200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>0.068900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>0.061400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>0.063500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>0.075700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>0.058300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>0.064900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>0.056700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>0.058100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>0.058200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.060700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>0.073600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>0.055800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>0.058600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>0.049500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>0.052500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>0.061500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>0.054000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>0.058600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>0.053700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.051800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161</td>\n",
              "      <td>0.063300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162</td>\n",
              "      <td>0.053100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163</td>\n",
              "      <td>0.050400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>0.051900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165</td>\n",
              "      <td>0.060700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166</td>\n",
              "      <td>0.050700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167</td>\n",
              "      <td>0.047400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168</td>\n",
              "      <td>0.052000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169</td>\n",
              "      <td>0.055000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.053800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171</td>\n",
              "      <td>0.052900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172</td>\n",
              "      <td>0.051000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173</td>\n",
              "      <td>0.048100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174</td>\n",
              "      <td>0.047700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.052600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176</td>\n",
              "      <td>0.057400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177</td>\n",
              "      <td>0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178</td>\n",
              "      <td>0.048400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179</td>\n",
              "      <td>0.052200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.049400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181</td>\n",
              "      <td>0.046700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182</td>\n",
              "      <td>0.052500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183</td>\n",
              "      <td>0.049100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184</td>\n",
              "      <td>0.046600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185</td>\n",
              "      <td>0.054600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>0.050100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187</td>\n",
              "      <td>0.055500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188</td>\n",
              "      <td>0.048300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189</td>\n",
              "      <td>0.043400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>0.047800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191</td>\n",
              "      <td>0.048100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192</td>\n",
              "      <td>0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193</td>\n",
              "      <td>0.047900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194</td>\n",
              "      <td>0.050300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195</td>\n",
              "      <td>0.049600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196</td>\n",
              "      <td>0.053400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197</td>\n",
              "      <td>0.047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198</td>\n",
              "      <td>0.051300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199</td>\n",
              "      <td>0.047100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.048800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Will smartly offload gradients to save VRAM!\n"
          ]
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCqnaKmlO1U9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4c83107-f32a-4732-d4ea-609975ba92b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1006.9078 seconds used for training.\n",
            "16.78 minutes used for training.\n",
            "Peak reserved memory = 4.141 GB.\n",
            "Peak reserved memory for training = 2.131 GB.\n",
            "Peak reserved memory % of max memory = 28.092 %.\n",
            "Peak reserved memory for training % of max memory = 14.456 %.\n"
          ]
        }
      ],
      "source": [
        "# @title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory / max_memory * 100, 3)\n",
        "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(\n",
        "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
        ")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model via Unsloth native inference! According to the `Qwen-3` team, the recommended settings for reasoning inference are `temperature = 0.6, top_p = 0.95, top_k = 20`\n",
        "\n",
        "For normal chat based inference, `temperature = 0.7, top_p = 0.8, top_k = 20`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kR3gIAX-SM2q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a6011c0-bd01-4588-e8f7-6b53c540a5b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，而新兴经济体整体表现稳定。<|im_end|>\n"
          ]
        }
      ],
      "source": [
        "messages = [\n",
        "    {\"role\" : \"user\", \"content\" : \"你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\\n你的任务是根据所获取的信息片段（<context></context>之间的内容）回答问题。\\n回答保持简洁，不必重复问题，不要添加描述性解释和与答案无关的任何内容。\\n已知信息：\\n<context>\\n2023年全球经济增长动力持续回落，各国复苏分化，发达经济体增速明显放缓，新兴经济体整体表现稳定。\\n</context>\\n\\n问题：2023年全球经济增长的特点是什么?\\n请回答：/no_think\"}\n",
        "]\n",
        "text = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = False,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    enable_thinking = False, # Disable thinking\n",
        ")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "_ = model.generate(\n",
        "    **tokenizer(text, return_tensors = \"pt\").to(\"cuda\"),\n",
        "    max_new_tokens = 256, # Increase for longer outputs!\n",
        "    temperature = 0.7, top_p = 0.8, top_k = 20, # For non thinking\n",
        "    streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "version = 'v5'"
      ],
      "metadata": {
        "id": "CYRAHWBsx6ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b7028a6-e520-4a39-d00d-2dfd70b2f5ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('lora_model_v5/tokenizer_config.json',\n",
              " 'lora_model_v5/special_tokens_map.json',\n",
              " 'lora_model_v5/vocab.json',\n",
              " 'lora_model_v5/merges.txt',\n",
              " 'lora_model_v5/added_tokens.json',\n",
              " 'lora_model_v5/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model.save_pretrained(f\"lora_model_{version}\")  # Local saving\n",
        "tokenizer.save_pretrained(f\"lora_model_{version}\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MKX_XKs_BNZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f8e891b-db6c-4ec8-a0dd-250e7ad46f0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth 2025.6.2: Fast Qwen3 patching. Transformers: 4.51.3.\n",
            "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
            " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        }
      ],
      "source": [
        "if True:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = f\"lora_model_{version}\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = 8192,\n",
        "        load_in_4bit = True,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHjt_SMYsd3P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
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        },
        "outputId": "a3f3219b-ab00-4fdd-afd1-867557723078"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found HuggingFace hub cache directory: /root/.cache/huggingface/hub\n",
            "Checking cache directory for required files...\n",
            "Cache check failed: model.safetensors not found in local cache.\n",
            "Not all required files found in cache. Will proceed with downloading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rUnsloth: Merging weights into 16bit:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.44G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2abe94da87240ddb5e3c9f2d1090e5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: Merging weights into 16bit: 100%|██████████| 1/1 [01:14<00:00, 74.01s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78b4718662ce4dce90458801f844d1d6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd799a70ddb8435caa43f4b5b682758c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found HuggingFace hub cache directory: /root/.cache/huggingface/hub\n",
            "Checking cache directory for required files...\n",
            "Cache check failed: model.safetensors not found in local cache.\n",
            "Not all required files found in cache. Will proceed with downloading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rUnsloth: Merging weights into 16bit:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.44G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a6d4541ea724e84b74ec9c470ae7aa2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Merge to 16bit\n",
        "if True:\n",
        "    model.save_pretrained_merged(f\"model_{version}\", tokenizer, save_method = \"merged_16bit\")\n",
        "if True: # Pushing to HF Hub\n",
        "    model.push_to_hub_merged(\"stevenluo/mastering_rag_ft_qwen3_1.7b\", tokenizer, save_method = \"merged_16bit\", token = os.environ['HUGGINGFACE_TOKEN'])\n",
        "\n",
        "# Merge to 4bit\n",
        "if False:\n",
        "    # model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "    model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit_forced\",)\n",
        "if False: # Pushing to HF Hub\n",
        "    model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if False:\n",
        "    model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
        "if False: # Pushing to HF Hub\n",
        "    model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
      },
      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
        "\n",
        "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FqfebeAdT073"
      },
      "outputs": [],
      "source": [
        "# Save to 8bit Q8_0\n",
        "if False:\n",
        "    model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
        "# And change hf to your username!\n",
        "if False:\n",
        "    model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False:\n",
        "    model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if False: # Pushing to HF Hub\n",
        "    model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False:\n",
        "    model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: # Pushing to HF Hub\n",
        "    model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
        "\n",
        "# Save to multiple GGUF options - much faster if you want multiple!\n",
        "if False:\n",
        "    model.push_to_hub_gguf(\n",
        "        \"hf/model\", # Change hf to your username!\n",
        "        tokenizer,\n",
        "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOfJSxs_VJjz"
      },
      "source": [
        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n",
        "\n",
        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
        "\n",
        "Some other links:\n",
        "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
        "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
        "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
        "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
        "\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
        "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
        "\n",
        "  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
        "</div>\n"
      ]
    }
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